View source: R/functions_multiple_tte.R
utility_multiple_tte | R Documentation |
The utility function calculates the expected utility of our drug development program and is given as gains minus costs and depends on the parameters and the expected probability of a successful program.
The utility is in a further step maximized by the optimal_multiple_tte()
function.
Note, that for calculating the utility of the program, two different benefit triples are necessary:
one triple for the case that the more important endpoint overall survival (OS) shows a significant positive treatment effect
one triple when only the endpoint progression-free survival (PFS) shows a significant positive treatment effect
utility_multiple_tte(
n2,
HRgo,
alpha,
beta,
hr1,
hr2,
id1,
id2,
c2,
c02,
c3,
c03,
K,
N,
S,
steps1,
stepm1,
stepl1,
b11,
b21,
b31,
b12,
b22,
b32,
fixed,
rho,
rsamp
)
n2 |
total sample size for phase II; must be even number |
HRgo |
threshold value for the go/no-go decision rule; |
alpha |
significance level |
beta |
|
hr1 |
assumed true treatment effect on HR scale for endpoint OS |
hr2 |
assumed true treatment effect on HR scale for endpoint PFS |
id1 |
amount of information for |
id2 |
amount of information for |
c2 |
variable per-patient cost for phase II |
c02 |
fixed cost for phase II |
c3 |
variable per-patient cost for phase III |
c03 |
fixed cost for phase III |
K |
constraint on the costs of the program, default: Inf, e.g. no constraint |
N |
constraint on the total expected sample size of the program, default: Inf, e.g. no constraint |
S |
constraint on the expected probability of a successful program, default: -Inf, e.g. no constraint |
steps1 |
lower boundary for effect size category |
stepm1 |
lower boundary for effect size category |
stepl1 |
lower boundary for effect size category |
b11 |
expected gain for effect size category |
b21 |
expected gain for effect size category |
b31 |
expected gain for effect size category |
b12 |
expected gain for effect size category |
b22 |
expected gain for effect size category |
b32 |
expected gain for effect size category |
fixed |
choose if true treatment effects are fixed or random, if TRUE |
rho |
correlation between the two endpoints |
rsamp |
sample data set for Monte Carlo integration |
The output of the function utility_multiple_tte()
is the expected utility of the program.
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